Digital Phenotyping in Clinical Neurology

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Exploring the Neuropsychiatric Sequalae of Perceived COVID-19 Exposure in College Students: A Pilot Digital Phenotyping Study

The urgency to understand the long-term neuropsychiatric sequala of COVID-19, a part of the Post-Acute COVID-19 Syndrome (PACS), is expanding as millions of infected individuals experience new unexplained symptoms related to mood, anxiety, insomnia, headache, pain, and more. Much research on PACS involves cross sectional surveys which limits ability to understand the dynamic trajectory of this emerging phenomenon. In this secondary analysis, we analyzed data from a 4-week observational digital phenotyping study using the mindLAMP app for 695 college students with elevated stress who specified if they were exposed to COVID-19. Students also completed a biweekly survey of clinical assessments to obtain active data. Additionally, passive data streams like GPS, accelerometer, and screen state were extracted from phone sensors and through features the group built. Three hundred and eighty-second number participants successfully specified their COVID-19 exposure and completed the biweekly survey. From active smartphone data, we found significantly higher scores for the Prodromal Questionnaire (PQ) and the Pittsburgh Sleep Quality Index (PSQI) for students reporting exposure to COVID-19 compared to those who were not (ps < 0.05). Additionally, we found significantly decreased sleep duration as captured from the smartphone via passive data for the COVID-19 exposed group (p < 0.05). No significant differences were detected for other surveys or passive sensors. Smartphones can capture both self-reported symptoms and behavioral changes related to PACS. Our results around changes in sleep highlight how digital phenotyping methods can be used in a scalable and accessible manner toward better capturing the evolving phenomena of PACS. The present study further provides a foundation for future research to implement improving digital phenotyping methods.

Digital phenotyping and the (data) shadow of Alzheimer's disease

In this paper, we examine the practice and promises of digital phenotyping. We build on work on the ‘data self’ to focus on a medical domain in which the value and nature of knowledge and relations with data have been played out with particular persistence, that of Alzheimer's disease research. Drawing on research with researchers and developers, we consider the intersection of hopes and concerns related to both digital tools and Alzheimer's disease using the metaphor of the ‘data shadow’. We suggest that as a tool for engaging with the nature of the data self, the shadow is usefully able to capture both the dynamic and distorted nature of data representations, and the unease and concern associated with encounters between individuals or groups and data about them. We then consider what the data shadow ‘is’ in relation to ageing data subjects, and the nature of the representation of the individual's cognitive state and dementia risk that is produced by digital tools. Second, we consider what the data shadow ‘does’, through researchers and practitioners’ discussions of digital phenotyping practices in the dementia field as alternately empowering, enabling and threatening.

Digital epidemiology, deep phenotyping and the enduring fantasy of pathological omniscience

Epidemiology is a field torn between practices of surveillance and methods of analysis. Since the onset of COVID-19, epidemiological expertise has been mostly identified with the first, as dashboards of case and mortality rates took centre stage. However, since its establishment as an academic field in the early 20th century, epidemiology’s methods have always impacted on how diseases are classified, how knowledge is collected, and what kind of knowledge was considered worth keeping and analysing. Recent advances in digital epidemiology, this article argues, are not just a quantitative expansion of epidemiology’s scope, but a qualitative extension of its analytical traditions. Digital epidemiology is enabled by deep and digital phenotyping, the large-scale re-purposing of any data scraped from the digital exhaust of human behaviour and social interaction. This technological innovation is in need of critical examination, as it poses a significant epistemic shift to the production of pathological knowledge. This article offers a critical revision of the key literature in this budding field to underline the extent to which digital epidemiology is envisioned to redefine the classification and understanding of disease from the ground up. Utilising analytical tools from science and technology studies, the article demonstrates the disruptive expectations built into this expansion of epidemiological surveillance. Given the sweeping claims and the radical visions articulated in the field, the article develops a tentative critique of what I call a fantasy of pathological omniscience; a vision of how data-driven engineering seeks to capture and resolve illness in the world, past, present and future.

The Use of Digital Phenotyping in Mental Health Services: Opportunities, Ethical and Legal Issues

The widespread use of smartphones, mobile devices, wearable technology, and the increase in time online has provided the opportunity to collect data about users continuously. Several sectors such as health, economy, and entertainment have benefitted from the digital traces left by users due to the tight interactions on digital platforms. Today, it is likely to determine and predict users' moods, behavioral patterns, habits, and personality traits with the use of digital traces that are processed using artificial intelligence techniques. Such use of digital data offers new opportunities for mental health services. Today with this method, it is possible to obtain simultaneous data on the course of the psychological disorder and create a complete and more holistic picture of the disorder by accessing data that can not be obtained from self-report assessment techniques. This new approach, which is called digital phenotyping, can improve the objectivity in diagnosis. Studies have proven digital phenotyping’s potential to determine disorders' recurrence risk and make psychometric predictions. The studies have indicated the promising future of digital phenotyping in mental health services since the initial discussions by Jain et al. in 2015. It is reported that digital phenotyping can be used to diagnose and follow certain mental health disorders such as depression, anxiety, and schizophrenia at an early stage. However, ethical concerns such as privacy, autonomy, data security, and data confidentiality are among the critical issues surrounding the use of digital phenotyping. This paper includes essential information about the digital phenotyping method, discussions about the practical, legal and ethical concerns regarding the use of digital phenotyping in mental health services, and suggestions for future research. Keywords: Digital phenotyping, mental health, smartphone, digital data, mental health services

Digital Phenotype of Mood Disorders: a conceptual and critical review (Preprint)

BACKGROUND Mood disorder is commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allow one to determine the "digital signature of a pathology". This strategy assumes that behaviors are "quantifiable" from data extracted and analyzed through digital sensors, wearable devices or smartphones. That concept could bring a shift for the diagnosis of mood disorder, introducing for the first time paraclinical testing on psychiatric routine care. OBJECTIVE The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of digital phenotypes applied to mood disorders. METHODS We conducted a selective review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with the relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. RESULTS 858 articles were included for evaluation, 43 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, body temperature). For depressive episodes, the main finding is the decrease in terms of functional and biological parameters (decrease in activities and walking, decrease in the number of calls and SMS, decrease in temperature and HRV) while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV). CONCLUSIONS The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders

Ethical, Legal and Social Issues of Digital Phenotyping as a Future Solution for Present-Day Challenges: A Scoping Review

Digital phenotyping represents an avenue of consideration in patients' self-management. This scoping review aims to explore the trends in the body of literature on ethical, legal, and social challenges relevant to the implementation of digital phenotyping technologies in healthcare. The study followed the PRISMA-ScR methodology (Tricco et al. in Ann Int Med 169(7):467–473, 2018. https://doi.org/10.7326/M18-0850). The review systematically identified relevant literature, characterised the discussed technology, explored its impacts and the proposed solutions to identified challenges. Overall, the literature, perhaps unsurprisingly, concentrates on technical rather than ethical, legal, and social perspectives, which limits understanding of the more complex cultural and social factors in which digital phenotyping technologies are embedded. ELS issues mostly concern privacy, security, consent, lack of regulation, and issues of adoptability, and seldom expand to more complex ethical issues. Trust was chosen as an umbrella theme of a continuum of major ELS and technical issues. Sustained critical analysis of digital phenotyping showed to be sparse and geographically exclusive. There is a continuum and overlap between ELS issues, suggesting the need for a holistic, interdisciplinary approach to each of the challenges posed by the various technologies of digital phenotyping.